The Indirect Cost of Natural Disasters and an Economic

IMPACT APPRAISAL FOR SOVEREIGN DISASTER RISK FINANCING AND
INSURANCE PROJECT: PHASE 1
PUBLIC FINANCE AND MACROECONOMICS, PAPER 3
The Indirect Cost of Natural Disasters and
an Economic Definition of
Macroeconomic Resilience
Stephane Hallegatte
Abstract
The welfare impact of a disaster does not depend only on the physical characteristics of the event
or its direct impacts in terms of lost lives and assets. Depending on the ability of the economy to
cope, recover and reconstruct, the reconstruction will be more or less difficult, and the welfare
effects smaller or larger. This ability, which can be referred to as the macroeconomic resilience of
the economy to natural disasters, is an important parameter to estimate the overall vulnerability of
a population. Here, resilience is decomposed into two components: instantaneous resilience, i.e. the
ability to limit the magnitude of the immediate loss of income for a given amount of capital losses;
and dynamic resilience, i.e. the ability to reconstruct and recover quickly. The paper proposes a rule
of thumb to estimate the macroeconomic resilience, based on the interest rate (a higher interest
rate decreases resilience and increases welfare losses), the reconstruction duration (a longer
reconstruction duration increases welfare losses), and a “ripple-effect” factor that increases or
decreases immediate losses (negative if enough idle resources are available to cope; positive if
cross-sector and supply-chain issues impair production of non-affected capital). An optimal risk
management strategy is very likely to include measures to reduce direct impacts (disaster risk
reduction actions) and measures to reduce indirect impacts (resilience building actions).
About the Project
The UK Department for International Development (DFID), the World Bank Group and the Global
Facility for Disaster Reduction and Recovery (GFDRR) have partnered to improve evaluation and
evidence for sovereign disaster risk financing and insurance (DRFI). The $3.2 million, 3-year (20132016) project was launched in 2013, and will develop and test a quantitative impact appraisal
framework for sovereign DRFI. The project aims to meet this need by developing a methodology to
evaluate a range of DRFI programs and provide quantitative results based on five country-specific
case studies, and seeks to understand whether forward-looking impact appraisals can help
effectively target support for disaster risk activities. The results will help better target and
prioritize future investments from national governments and international donors in sovereign
DRFI programs. This paper forms part of the background research underpinning the draft
operational framework, to be pilot tested in the case studies in Phase 2 of the project.
The indirect cost of natural disasters and an economic definition of
macroeconomic resilience1
Stephane Hallegatte
The World Bank
Sustainable Development Network – Office of the Chief Economist
[email protected]
1 Introduction
A natural disaster is not a “natural” event. Human and natural systems are affected by natural hazards,
such as earthquakes, storms, hurricanes, intense precipitations and floods, droughts, landslides, heat
waves, cold spells, and thunderstorms. If a hazard affects a human system – from one house to one
region – and causes sufficiently larger negative consequences to this system, the event can then be
labelled as a natural disaster. But a disaster occurs only when there is the conjunction of a natural event
– the hazard – and a human system, leading to negative consequences. As such, what we call a natural
disaster is thus above all a social and human event (World Bank 2010).
From an economic perspective, a natural disaster can be defined as a natural event that causes a
perturbation to the functioning of the economic system, with a significant negative impact on assets,
production factors, output, employment, or consumption. There are multiple formal definitions. The
Center for Research on the Epidemiology of Disasters (CRED) at the Catholic University of Louvain
defines a disaster as a natural situation or event which overwhelms local capacity and/or necessitates a
request for external assistance.2 When it happens, however, the perturbation affects the economic
system in a way that goes beyond the loss of asset and the monetary expenditures to replace damaged
property. Secondary consequences include the loss of output and production, rationing in some sectors,
loss of employment and tax revenues, etc. These consequences need to be accounted for to assess the
disaster impact on the population’s well-being and welfare.
This paper defines and discusses the economic consequences of natural disasters. It summarizes the
most important mechanisms that determine the cost of disaster, and explain why the direct economic
1
This article benefited from inputs from Valentin Przyluski and from comments and suggestions from Paolo
Bazzurro, David Bevan, Laura Bonzanigo, Sebastian Boreux, Daniel Clarke, Sebastian von Dahlen, Patrice Dumas, Ulf
Narloch, Richard Poulter, Julie Rozenberg, Tse-Ling The, and Adrien Vogt-Schilb. It has been developed as a
background paper for the “Sovereign DRFI impact appraisal project”, funded by UKaid, The World Bank, the
Disaster Risk Financing and Insurance Program, and the Global Facility for Disaster Reduction and Recovery
(GFDRR).
2
For a disaster to be listed in the EM-DAT database, at least one of the following criteria should be met: (i) 10 or
more people are reported killed; (ii) 100 people are reported affected; (iii) a state of emergency is declared; (iv) a
call for international assistance is issued.
1
cost, i.e. the value of what has been damaged or destroyed by the disaster, is not a sufficient indicator
of disaster seriousness and why estimating indirect losses is crucial. Then, it discusses the
methodologies and the models that are able to measure indirect losses and proposes a simple rule of
thumbs to take them into account when time and resources do not allow for a full modelling exercise.
Finally, the paper discusses the tools that are available to increase the resilience of the economic
system, i.e. to make it better able to recover and reconstruct and to reduce the indirect consequences
of disasters.
Thorough this paper, boxes will present an application of the methodologies on one case study
conducted in an OECD project, on flood risk in Mumbai.3 The study investigates the vulnerability to
heavy precipitations of the city of Mumbai, a rapidly-growing coastal city in a developing country. The
complete study is available in (Ranger et al., 2011), and a longer version with a survey of the slum
dwellers affected during the 2005 floods in Mumbai is in (Hallegatte et al., 2010).
2 The economic cost of a disaster
2.1 Defining the economic cost of extreme events
Defining the economic cost of a disaster poses different theoretical and practical challenges. After each
large-scale disaster, media, insurance companies and international institutions publish numerous
assessments of the “cost of the disaster.” These various assessments are based on different
methodologies and approaches, and they often reach quite different results. In the US, for instance, a
systematic analysis by (Downton and Pielke, 2005) showed that loss estimates differ by a factor of 2 or
more for half of the floods that cause less than $50 million in damages. These discrepancies are in part
due to technical and practical problems, but also to the multi-dimensionality in disaster impacts and
their large redistributive effects and the fact that the boundary of assessment dependent on the
purpose of the assessment.4 But the purpose of these assessments is rarely specified, even though
different purposes correspond to different perimeters of analysis and different definitions of what a cost
is.
This confusion translates into the multiplicity of words to characterize the cost of a disaster in published
assessments: direct losses, asset losses, indirect losses, output losses, intangible losses, market and nonmarket losses, welfare losses, or some combination of those. It also makes it almost impossible to
compare or aggregate published estimates that are based on many different assumptions and methods.
2.1.1
Direct and indirect costs
Many authors have discussed typologies of disaster impacts (e.g., Cochrane, 2004; Lindell and Prater,
2003; Pelling et al., 2002; Rose, 2004). These typologies usually distinguish between direct and indirect
3
The study was carried out by a consortium including the OECD, Risk Management Solutions, CIRED, Météo-France,
NATCOM PMC, and the Indian Institute for Technology Bombay at Mumbai, and published in Ranger et al. (2011).
4
For instance, an analysis can be carried out at national scale to estimate the impact on macroeconomic
aggregates (e.g., GDP, tax revenues) and at local scale to estimate local welfare losses and the need for support.
The latter will not include potential benefits outside the affected areas; the former will include them.
2
losses. Direct losses are the immediate consequences of the disaster physical phenomenon: the
consequence of high winds, of water inundation, or of ground shaking. Typical examples include roofs
that are destroyed by high winds, cars destroyed and roads washed away by floods, injuries and
fatalities from collapsed buildings. Direct losses are often classified into direct market losses (for goods
that can be bought on a market, such as cars and buildings) and direct non-market losses (for what
cannot be bought on a market, like human lives and ecosystems).
Box 1 – Direct flood losses in Mumbai
On Mumbai, the flood risk analysis begins with an analysis of past high precipitation events, to assess
their probability of occurrence. It then assesses the impact of climate change on future heavy
precipitation statistics using downscaling techniques (namely, the WXGEN weather generator). Next, it
translates the statistics of heavy precipitation into river run-off and flood hazards, using the Storm
Water Management Model (SWMM). The result is an assessment of the probability of various flood
events. This exercise produces flood maps, in the current climate and in the climate projected by the
PRECIS climate model with the IPCC/A2 emission scenario (see box figure).
Figure. Flood map corresponding to the 200-year return period precipitation event, in the Mithi basin, in
Mumbai, today (left panel) and in the 2080’s in one climate scenario (right panel).
Then, the population and assets exposed to flood risks is assessed, using data on population and assets
collected by Risk Management Solutions from an insurance database developed for the assessment of
earthquake risks.
3
In the absence of vulnerability curves for the buildings that can be found in Mumbai, the analysis uses
“average damage ratio”. It is assumed that when a property is flooded, a constant share of its value is
lost, regardless of the water level and the detailed characteristics of buildings. Using three different
techniques (based on published loss estimates for the 2005 floods, insurance data for the 2005 floods,
and simple vulnerability curves), these damage ratios are estimates at 5–15% for residential properties,
15–35% for commercial properties, and 10–30% for industrial properties. In the absence of data on
infrastructure, a constant ratio of infrastructure to non-infrastructure losses is introduced. Based on
previous events in various world cities, this ratio is taken at 40% (i.e., where there is $1 of noninfrastructure losses, there is $0.40 of infrastructure losses).
From this, the analysis can then provide an estimate of the direct economic losses that corresponds to
various probability of occurrence, expressed in terms of return periods. For instance, the losses due to
the 50-yr return period flood are estimated between $210 and $570 million.
To complete this analysis with information on how these losses are distributed in the population and
especially on the poorest, a survey was conducted with informal dwellers affected by the 2005 floods. It
was found that the aggregated losses they suffered from are about $250 million (for total losses due to
the 2005 flood of about $2 billion), but the relative impact on their savings and consumption was
extremely large, with average capital losses of the same order of magnitude than their average total
savings (in other terms, their savings were totally wiped out by the event).
Indirect losses (also labelled “higher-order losses” in Rose, 2004) include all losses that are not provoked
by the disaster itself, but by its consequences; they are spanning over a longer period of time than the
event, and they affect a larger spatial scale or different economic sectors5. Like direct losses, indirect
losses can be market or non-market losses. They include several categories of losses, such as:

Output losses (including business interruptions, supply-chain disruptions, and lost production due to
capital damages) often represent a large share of indirect losses. The Iceland volcano eruption in
2010 interrupted air transport for a week, i.e. cancelled air transport over the North Atlantic even in
absence of any capital loss. A damaged factory after a hurricane cannot produce until it is rebuilt or
repaired, leading to output losses. Output losses are also due to complex interactions between
businesses, such as production bottlenecks when one element of a supply chain is affected and
paralyze the entire production process.

Macro-economic feedbacks, include the impact of reduced final demand because consumers and
businesses suffer from a reduced income (e.g., due to loss of jobs), and the effect of lost tax revenue
on public demand. Long-term adverse consequences on economic growth are also possible because
of changes in risk perception (including over-reactions) that can drive investors and entrepreneurs
out of the affected area.
5
Unsurprisingly, different hazards communities have different approaches for defining indirect costs. Contentious
issues may emerge around the edge of these definitions across hazard communities.
4
The impact on poverty or inequalities is also sometimes included in the indirect losses. The landfall of
Katrina on New Orleans has renewed attention on the larger weather vulnerability of the poorest
communities within a country, and on the inequality-widening effect of disasters (Atkins and Moy, 2005;
Tierney, 2006). Rodriguez-Oreggia et al. (2009) show that municipalities affected by disasters in Mexico
see an increase in poverty by 1.5 to 3.6 percentage point. Often, the poorest have little to lose in a
disaster and the impact on their welfare is therefore invisible in aggregated economic statistics. If the
aim of the assessment is to look at welfare impacts, focusing only on economic aggregates can be
misleading.
Some of these impacts can be captured using classical economic indicators, such as GDP. There are
however several issues when using GDP change as an indicator for indirect losses. A first question deals
with the spatial scale: for large countries, the scale of the event and the scale of GDP measurement are
very different, and a large shock for local populations can hardly be visible on national GDP. It does not
mean, however, that welfare impacts are negligible. Second, GDP is known to be a poor proxy for welfare
(Fleurbaey, 2009).
An obvious illustration of why indirect losses are important is the difference between disaster scenarios
with various reconstruction paces. In terms of welfare, there is a large difference between, on the one
hand, a scenario in which all direct losses can be repaired in a few months thanks to an efficient
reconstruction process and, on the other hand, a scenario in which reconstruction is inefficient and
takes years.
2.1.2
Assessing indirect losses: defining a baseline
A first difficulty in disaster indirect cost assessment lies in the definition of the baseline scenario. The
cost of the disaster has indeed to be calculated by comparing the actual trajectory (with disaster
impacts) with a counterfactual baseline trajectory (i.e., a scenario of what would have occurred in the
absence of disaster). This baseline is not easy to define, and several baselines are often possible.
Moreover, in cases where recovery and reconstruction do not lead to a return to the baseline scenario,
there are permanent (positive or negative) disaster effects that are difficult to compare with a nondisaster scenario.
For instance, a disaster can lead to a permanent extinction of vulnerable economic activities in a region,
because these activities are already threatened and cannot recover, or because they can move to less
risky locations. In that case, the disaster is not a temporary event, but a permanent negative shock for a
region. Also, reconstruction can be used to develop new economic sectors, with larger productivity, and
lead to a final situation that can be considered more desirable than the baseline scenario. This
improvement can be seen as a benefit of the disaster. It is however difficult to attribute unambiguously
this benefit to the disaster, because the same economic shift would have been possible in the absence
of disaster, making it possible to get the benefits without suffering from the disaster-related human and
welfare losses.
For instance, hurricanes in 1806 and 1807 participated in the shift that occurred at la Réunion – a French
island in the Indian Ocean – from coffee to sugar cane production. Indeed, it takes about 5 years for a
5
coffee plant to start producing usable fruits. When the hurricanes hit, the need to start producing again
as soon as possible drove farmers to plant sugar cane, an annual crop that can be harvested in a year.
Moreover, coffee production was then considered more vulnerable to wind damages than sugar cane.
But sugar cane production also has a different economic vulnerability to other stresses such as changes
in food markets, consumer tastes, and agricultural subsidies. Therefore, it is difficult to assess whether
the shift from coffee to sugar cane should be considered as a cost of the hurricanes (if sugar cane
eventually reveals less profitable than coffee) or as a benefit (if sugar cane is more profitable than
coffee). The final outcome depends on many other factors that can hardly be predicted at the time of
the disaster.
This baseline issue – very common in economics – is not easy to deal with, and different scholars have
used different techniques. (Coffman and Noy, 2011) use two nearby islands to assess the impact of
hurricane Iniki. Since the hurricane affected only one island, the other can be used as a “control”, i.e. as
a proxy for the economic condition of the affected island if it had been spared. But such convenient
control is not always available, making it necessary to construct the counterfactual, for instance using
modelling tools (DuPont and Noy, 2012).
2.2 Output losses and their drivers
Damages to assets make them unable to produce: a damaged factory cannot build cars, a damaged road
cannot be used, and a damaged house cannot be inhabited. The first step in an assessment of output
losses is to estimate how much output is lost because of these direct asset losses.
2.2.1
From asset losses to output losses
Economic theory states that, at the economic equilibrium and under certain conditions, the value of an
asset is the net present value6 of its expected future production. In this case, the annual loss of output is
equal to the value of the lost capital multiplied by the marginal productivity of capital (which is equal to
the interest rate, increased by the depreciation rate). Assuming this equality is always verified, the
output loss caused by capital loss is simply equal to the value of the lost asset, and summing the two is a
double count.
Figure 1 illustrates this point in a scenario in which no reconstruction takes place: in that case, the
production that is lost because of the disaster is equal to the value of the lost assets.7 In estimates of
disaster consequences, what is referred to as “asset loss” is the replacement value of the capital. To
have the equality of asset loss and output loss, a double equality needs to be verified: replacement
value has to be equal to market value; and market value has to be equal to the net present value of
expected output.
6
Note that the net present value is the sum of the production, discounted to account for the fact that production
far in the future has less value than more immediate production.
7
Note also the baseline issue: maintaining unchanged production requires investments to maintain the capital
stock; the lower production level post-disaster is possible with a reduced investment level, thereby creating a
benefit. This is taken into account by the fact that the marginal productivity of capital used to calculate asset value
should include the depreciation rate.
6
In a theoretical and optimal economy at equilibrium – these two equalities are valid. First, if the market
value of an asset is lower (resp. larger) than the net value of its output, then investors will buy more
(resp. sell) more of this asset to capture the difference in value, making asset price increase (resp.
decrease). Second, if market value were higher (resp. lower) than replacement value, then investors
would increase (resp. decrease) the amount of physical capital to restore the equality between market
and replacement value (assuming decreasing returns). In a realistic setting, however, these two
assumptions are not always verified. The reasons why asset values and output losses can differ are
discussed below.
Production
DISASTER
Baseline production
(no disaster)
“discounted” area = value of lost
assets
Actual production (with disaster
and no reconstruction)
Time
Figure 1. Production as a function of time, without disaster or in a scenario with disaster and no
reconstruction. In the latter case, the discounted value of the lost production (from the disaster to the
infinity) is equal to the value of lost assets. The production decrease is equal to the value of lost assets
multiplied by the interest rate.
The economy is not at its optimum. For the replacement value and the market value to be equal, the
economy needs to be at its optimum, i.e. the amount of capital is such that its return is equal to the
(unique) interest rate. This is not always the case especially in sectors affected by disasters. In some
sectors, expectations can be heavily biased (e.g., in the housing market) and markets distorted, leading
to large differences between capital returns and interest rate. This is also unlikely for infrastructure and
public assets. Since these assets are not exchanged on markets, they have no market prices. Moreover,
they are not financed by decisions of private investors using financial returns, but by government
decisions through a political process taking into account multiple criteria (e.g., land-use planning
objectives). Furthermore, output losses need to be estimated from a social point-of-view. The equality
between market value (for the owner) and expected output (for the society) is valid only in absence of
7
externalities. Some assets that are destroyed by disasters may exhibit positive externality. It means that
their value to the society is larger than the value of the owner’s expected output. Public goods have this
characteristic, among which most infrastructures, health services, education services.
An example is provided by the San Francisco Oakland Bay Bridge, which is essential to economic activity
in San Francisco and had to be closed for one month after the Loma Prieta earthquake in 1989. Its
replacement value has no reason to be equal to the loss in activity caused by the bridge closure, because
the bridge production is not sold on a market, the bridge has no market value, and the social return on
capital of the bridge is unlikely to exhibit decreasing returns and is likely to be much higher than the
interest rate. Another example is the health care system in New Orleans. Beyond the immediate
economic value of the service it provided, a functioning health care is necessary for a region to attract
workers (what economists call a “positive externality”). After Katrina’s landfall on the city, the lack of
health care services made it more difficult to reconstruct, and the cost for the region was much larger
than the economic direct value of this service.
The shock is large (“non-marginal” in economic terms). The equality of asset value and output is valid
only for marginal changes, i.e. for small shocks that do not affect the structure of the economy and the
relative prices of different goods and services. The impact is different for large shocks. Most assets have
“decreasing returns”, i.e. their productivity decreases with the total amount of asset. For instance, if
there are one million cars in a city, the loss of one car is a marginal shock, and the output of this car
should be equal (at the optimum) to the production cost of a car. But in practice, some cars have a
larger productivity than others: some cars are driven 1000 km per year while others are driven 80,000
km per year; clearly the latter car is more productive than the former. In economic theory, the least
productive car – i.e. the one that is driven the shortest distance per year – has an output equal to the
production cost of cars. All the other ones have a higher productivity. As a result, the destruction of one
car – assuming the least productive one is destroyed – leads to an output loss equal to the replacement
cost of the car. But the destruction of many cars will affect cars with various productivity levels, and
leads to an output loss that is larger than the replacement value of these cars. Moreover, the equality of
asset value and output depends on the assumption that the destructions affect the least productive
assets only. In the previous example, it is assumed that if one car is destroyed, then it is the least
productive (i.e. the one that is driven the shortest distance per year). Or equivalently, it is assumed that
the owner of the destroyed car will instantaneously buy the least used car to its owner (which makes
sense because the former makes a more efficient use of the car than the latter).
To account for these effects, it is more realistic to assess the loss of output as the value of lost assets
multiplied by the average productivity of capital, instead of the marginal productivity. Using classical
production function and parameters, this approximation leads to output losses equal to approximately
three times the value of lost assets; see details in (Hallegatte et al., 2007), and Appendix A.
Asset and output losses are often estimated assuming unchanged (pre-disaster) prices, which is valid
only for marginal shocks. One can assume that if a house is destroyed, the family who owns the house
will have to rent another house at the pre-disaster price. In other terms, the value of the housing service
provided by the house can be estimated by the rental cost of a similar house before the disaster. But this
8
assumption is unrealistic if the disaster causes more than a small shock. In post-disaster situations,
indeed, a significant fraction of houses may be destroyed, leading to changes in the relative price
structure. In this case, the price of alternative housing can be much higher than the pre-disaster price, as
a consequence of the disaster-related scarcity in the housing market. For large shocks, estimating the
value of lost housing service should take into account the price change. Compared with an assessment
based on the pre-disaster prices, it can lead to a significant increase in the assessed disaster cost. The
same reasoning is possible in all other sectors, including transportation, energy, water, health, etc.
In extreme cases, there may be rationing, i.e. the price cannot clear the market and supply is not equal
to demand. This is because markets are not at equilibrium in disaster aftermath). The « If I can pay it, I
can get it » assumption is not valid in post-disaster situations (e.g., there is no available house for rent at
any price, there is no qualified worker to repair a roof). In these situations, therefore, the welfare impact
of lost production cannot be estimated as the product of lost produced quantity and pre-disaster prices.
Providing an unbiased estimate requires an assessment of the disaster impact on prices and taking into
account rationing. Appendix B provides details.
Post-disaster price inflation (also referred to as “demand surge”) is especially sensible in the construction
sector, which sees final demand soar after a disaster. For instance, Figure 2 shows the large (and
persistent) increase in wages for roofers in an area heavily affected by hurricane losses in Florida in 2004.
Annualized wage change in Miami, FL
20.00%
All occupations
Roofers
15.00%
10.00%
5.00%
Oct-06
Aug-06
Jun-06
Apr-06
Feb-06
Dec-05
Oct-05
Aug-05
Jun-05
Apr-05
Feb-05
Dec-04
Oct-04
Aug-04
Jun-04
Apr-04
Feb-04
Dec-03
Oct-03
-5.00%
Aug-03
0.00%
Figure 2: Roofer wages in an area where losses have been significant after the 2004 hurricane season in
Florida. Data from the Bureau of Labor Statistics, Occupational Employment Surveys in May 03, Nov 03,
May 04, Nov 04, May 05, May 06, May 07.
2.2.2
“Ripple effects”
Output losses are not only due to forgone production from the assets that have been destroyed or
damaged by the event. Assets that have not been affected by the hazards can also reveal unable to
produce at the pre-event level because of secondary effects, sometimes referred to as “ripple effects”.
9
This is particularly the case for infrastructure and utility services (electricity, water and sanitation, gas,
etc.). In past cases, it has been shown that the loss of utility services had larger consequences than direct
asset losses, both on households (McCarty and Smith, 2005) and on businesses (Gordon et al., 1998;
Tierney, 1997).
(McCarty and Smith, 2005) investigated the impact of the 2004 hurricane season on households in
Florida, and find that among the 21% of the households who were forced to move after the disaster, 50%
had to do so because of the loss of utilities (e.g., they had no running water). In only 37% of the cases,
the main reason was structural damages to the house. (Tierney, 1997) and (Gordon et al., 1998)
investigate the impact of the Northridge earthquake in 1994 in Los Angeles; they find also that loss of
utility services and transport played a key role. Tierney surveys the reasons why small businesses had to
close after the earthquake. The first reason, with 65% of the answers (several answers were possible), is
the need for clean-up. After that, the five most important reasons are loss of electricity, employee
unable to get to work, loss of telephones, damages to owner’s or manager’s home, and few or no
customers, with percentages ranging from 59% to 40%. All these reasons are not related to structural
damages to the business itself, but to offsite impacts. (Gordon et al., 1998) ask businesses to assess the
earthquake loss due to transportation perturbations, and find that this loss amounts to 39% of total
losses. (Kroll et al., 1991) find comparable results for the Loma Prieta earthquake at San Francisco in
1989: the major problems for small business were customer access, employee access, and shipping
delays, not structural damages. Utilities (electricity, communication, etc.) caused problems, but only over
the short term, since these services have been restored rapidly; only transportation issues have led to
long lasting consequences.
In theoretical terms, this effect arises from the fact that the capital is non-homogeneous and capital
components are not perfectly substitutable. As a result, the consequences of disaster capital losses
depend not only on the amount of capital that is lost, but also on which type of capital is lost. If the
stock of capital consists of an ensemble of capital categories that have some complementarity, then the
destruction of one component may reduce the productivity of other components and thus have an
impact that is larger than what could be expected from the analysis of one component only (see
Appendix A). One extreme example is the case of a road that is built out of a series of segments between
point A and B: if one segment is destroyed, then the road is not usable and the other segments become
useless. The output loss due to the destruction of one segment cannot be estimated based on the
analysis of one segment, but requires an analysis of the entire system (the road). The same is true – at
various degrees – of the entire economic system: the loss of one component can affect the other
component and lead to losses that are higher (or lower) than the value of the asset loss suggests
depending on the substitutability.
10
Output losses are also due to complex interactions between businesses. Business perturbations may
indeed also arise from production bottlenecks through supply-chains of suppliers and producers.8 These
ripple-effects can be labelled “backward” or “forward”:
 Backward ripple-effects arise when the impact propagates from clients to suppliers, i.e.
when a business cannot produce, and thus reduces its demand to its suppliers, reducing
their own activity (even in absence of direct damages).
 Forward ripple-effects arise when the impact propagates from suppliers to clients, i.e. when
a business cannot produce and thus cannot provide its clients with inputs needed for their
own production.
The output losses due to a disaster depend on the characteristics of the firm-to-firm networks (Henriet
et al., 2011), such as the average number of suppliers that firms have, or the shape and structure of the
connection between firms. These results suggest that modern economies, with global supply chains,
limited number of suppliers and small stocks, may be more vulnerable to natural disasters than
traditional, close economies. But the model used in (Henriet et al., 2011) is too simple for providing
realistic assessment of disaster costs, and detailed information on real-world economic networks is not
available. The impact of disasters on supply chains are tragically illustrated by the recent Tohoku-Pacific
earthquake in Japan, and its wide consequences on industrial production and exports, especially in the
auto industry. As an example, The Economic Times, an Indian newspaper, reports that “Japan's Toyota
Motor will cut production at its Indian subsidiary by up to 70% between April 25 and June 4 due to
disruption of supplies.”
When capital cannot produce because of a lack of input (e.g., electricity, water), several options are
available: input substitution, production rescheduling, mobilization of existing idle resources, and longer
work hours can compensate for a significant fraction of the losses (Rose et al., 2007). Loss of output in
the affected area and during the disaster aftermath could thus be compensated by increased production
outside the affected area (e.g., when another region capture the market shares lost by the affected
region) or later (e.g., when production is rescheduled after basic services are restored). Even within the
affected area, output losses from destroyed capital can be compensated by increased production from
factories and production units that did not suffer from losses and had idle capacity before the event
(which depends on the pre-existing situation, see next section).
These mechanisms can damp output losses, and can especially reduce the crowding-out effects of
reconstruction on normal consumption and investment. But their ability to do so is limited, especially
when losses are large. In case of large disasters, output losses will be largely dependent on two
characteristics of the economy: the adaptability and flexibility of its production processes; and its ability
to channel economic production toward its most efficient uses.
8
These ripple effects can even take place within a factory, if one segment of the production process is impossible
and therefore interrupts the entire production.
11
2.2.3
Stimulus effect
Disasters lead to a reduction of production capacity, but also to an increase in the demand for the
reconstruction sector and goods. Thus, the reconstruction acts in theory as a stimulus. However, as any
stimulus, its consequences depend on the pre-existing economic situation, such as the phase of the
business cycles and the existing of distortion that lead to under-utilization of production capacities.
If the economy is efficient and in a phase of high growth, in which all resources are fully used, the net
effect of a stimulus on the economy will be negative, for instance through diverted resources, production
capacity scarcity, and accelerated inflation. If the pre-disaster economy is depressed, on the other hand,
the stimulus effect can yield benefits to the economy by mobilizing idle capacities.
Economies in recession are more resilient to the effects of natural disasters (see a model based analysis
in Hallegatte and Ghil, 2008). This result appears consistent with empirical evidence. For instance, the
1999 earthquake in Turkey caused direct destructions amounting to 1.5 to 3% of Turkey’s GDP, but
consequences on growth remained limited, probably because the economy had significant unused
resources at that time (the Turkish GDP contracted by 7% in the year preceding the earthquake). In this
case, therefore, the earthquake may have acted as a stimulus, and have increased economic activities in
spite of its human consequences. In 1992, when hurricane Andrew made landfall on south Florida, the
economy was depressed and only 50% of the construction workers were employed (West and Lenze,
1994). The reconstruction needs had a stimulus effects on the construction sectors, which would have
been impossible in a better economic situation (e.g., in 2004 when four hurricanes hit Florida during a
housing construction boom).
The stimulus therefore exists only if there is idle capacity in the economy, i.e. some distortions in the
economic system. In developing countries, where capital is scarce and (unskilled) labor abundant, it is
unlikely that large idle capacity exist, except during recessions. However, many developing country
economies are plagued with large distortions that sometimes lead to excess investments in capital. In
that latter case, it is possible that idle capacity are available and that the reduction in capacity due to a
disaster has no impact.
The stimulus benefits may be considered as a positive outcome of disasters, but it should not. Indeed,
the same stimulus benefits could be captured in the absence of a disaster, through a standard stimulus
policy, and without the negative welfare and human impacts that come with disasters. For instance,
output may be stimulated by the reconstruction of many houses destroyed by a hurricane; but the same
output generation is possible by building new and better housing or by retrofitting existing building to
make them more energy efficient, without the need for any destruction. The possibility of a stimulus
effect would only reflects the fact that pre-existing economic policy is inappropriate, and this could be
corrected independently of a disaster. So, it would be improper to attribute this positive effect to the
disaster itself.
12
2.2.4
Non-linearity in output losses
There are three main reasons why output losses are likely to increase non-linearly with the size of the
disaster (and the amount of destruction):
- First, all economies have idle capacities (e.g., factories that do not produce as much as they
technically can, and workers who could and sometime wish to work more hours). If lost
production by affected capital is small enough to be fully compensated by increased production
from non-affected idle capacity, then there is no output loss (Albala-Bertrand, 2013). As a result,
output losses appear only if direct losses are larger than a given threshold that depends on the
pre-existing economic situation.
- Second, the “ripple effects” from infrastructure to firms and households and across firms are
also likely to increase with the number of affected firms (and the individual loss of output)
(Henriet et al., 2011).
- Third, the reconstruction capacity is always limited by financial and technical constraint and it
makes rebuilding after a large scale disaster much longer than after a small one (Benson and
Clay, 2004). In other terms, the duration of the shock increases with its amplitude. As a result,
the output losses – that depend on the magnitude of the shock and its duration – will increase
more than proportionally with direct losses.
The amount of damages can be a misleading indicator of the reconstruction duration. The 10 billion
Euros of reconstruction expenditures after the 2002 floods in Germany correspond to 10 days of total
German investments. But reconstruction has been spread out over more than 3 years, suggesting that
only a small fraction of investments can be dedicated to reconstruction (even though the return on
investment from reconstruction should theoretically be higher than other investments, as suggested in
Appendix A), because of financial and technical constraints.
A model-based investigation of this issue using the Adaptive Regional Input-Ouput (ARIO) model
(Hallegatte, 2013, 2008) concludes that total losses due to a disaster affecting Louisiana increase
nonlinearly with respect to direct losses when direct losses exceed $50 billion (see Figure 3). When
direct losses are lower than $50bn, aggregated indirect losses are close to zero (even though the
aggregation hides important disparities among sectors and among social categories). Beyond $50 billion
of direct losses, indirect losses increase nonlinearly. When direct losses exceed $200 billion, for instance,
total losses are twice as large as direct losses. For risk management, therefore, direct losses are
insufficient measures of disaster consequences.
13
Indirect losses (US$b)
400
350
300
250
200
150
100
50
0
-50
0
100
200
Direct losses (US$b)
300
Figure 3: The direct losses – output losses as a function of direct (asset) losses, in Louisiana for Katrinalike disasters of increasing magnitude.
The output losses due to a hazard (and the resulting welfare impact) do not depend only on the physical
intensity of the natural event, but also on the coping capacity of the affected human system. Physical
measures of disaster intensity (e.g., in m/s for windstorm) or even measures of direct losses (e.g., the
number and value of destroyed and damaged buildings) are very poor proxies of the real economic cost
of a disaster. A corollary is that natural disasters can have significant macroeconomic impacts, when they
are larger than the (context specific) economic coping capacity. Moreover, it means that reducing natural
disaster impacts on welfare can be done through an increase of the coping capacity, to make
reconstruction faster and more efficient and limit output losses.
2.3 Assessing indirect losses
2.3.1
Measuring indirect losses using econometric analyses
Econometrics analyses have been used to measure output losses, understood as reduction in GDP
following a disaster, but they reach contradictory conclusions (see great reviews in Dell et al., 2013;
Kousky, 2014). Albala-Bertrand (1993) and Skidmore and Toya (2002) suggest that natural disasters have
a positive influence on long-term economic growth, probably thanks to the stimulus effect of
reconstruction (section 1.2.3) and possibly the replacement of damaged capital with more recent
technologies (on this “productivity effect”, see also critical discussions in Benson and Clay, 2004;
Hallegatte and Dumas, 2009). Others, like (Hochrainer, 2009; Jaramillo, 2010; Noy and Nualsri, 2007;
Noy, 2009; Raddatz, 2009) suggest that the overall impact of disasters on growth is negative. At local
scale, (Strobl, 2010) investigates the impact of hurricane landfall on county-level economic growth in the
14
US and shows that a county that is stuck by at least one hurricane over a year sees its economic growth
reduced on average by 0.79 percentage point (and increased by only 0.22 percentage point the following
year). On Vietnam, (Noy and Vu, 2010) investigate the impact of disasters on economic growth at the
province level, and find that lethal disasters decrease economic production while costly disasters
increase short-term growth.
The lack of consensus on the impact of disasters on GDP is likely to arise from different impacts from
small and large disasters, the latter having a negative impact on growth while the former enhance
growth, and from different impacts from different types of disasters. For instance, (Felbermayr and
Gröschl, 2013) find that disasters in the top decile in terms of magnitude lead on average to a 3 percent
reduction in GDP growth. The loss is only 1.5 percent for disasters in the top 15% percentile, and 0.8%
for disasters in the top 20%. For smaller disasters, no impact can be detected. The type of disaster also
matters: (Loayza et al., 2012) find that droughts reduce GDP growth by 1.7 percentage point, while
floods increase GDP by 0.5% (possibly because floods enrich soils and increase agricultural productivity).
2.3.2
Modeling indirect losses
Many scholars have used economic models to estimate output losses. Many different models have been
used, but the most common are Input-Output (IO) or Calculable General Equilibrium (CGE) models. In
these models, the economy is described as an ensemble of economic sectors, which interact through
intermediate consumptions. These models however describe differently how these different sectors
interact with each other, and how they react to shocks.
Some models are based on the Input-Output (IO) linear assumption (Leontief, 1951), in which the
production of one unit in one sector requires a fixed amount of inputs from other sectors, and in which
prices do not play any role. It includes (Bockarjova et al., 2004; Haimes and Jiang, 2001; Hallegatte,
2013, 2008; Okuyama, 2004; Okuyama et al., 2004; Santos and Haimes, 2004). Other models are based
on the Calculable General Equilibrium (CGE) framework, which assumes that changes in relative prices
balance supply and demand in each sector (Rose and Liao, 2005; Rose et al., 2007).
Economic losses caused by a disaster are smaller in a CGE setting than in an IO setting. It is often
considered that IO models represent the short-term economic dynamics, in which production
technologies are fixed and prices cannot adjust. CGE models, on the other hand, represent the longterm dynamics, in which flexibility in production processes and markets allow for an adjustment of the
economic system. In reality, it is likely that IO models are pessimistic in their assessment of disaster
output losses, because there is flexibility even over the short term (for instance, maintenance can be
postponed; workers can do more hours to cope with the shock; production can be rescheduled, see
(Rose et al., 2007)). It is also likely that CGE models are optimistic, even in the long run, because prices
have stickiness and cannot adjust perfectly, and because substitution has technical limits that are not
always adequately represented in production functions.
Considering the sensitivity of model results to many parameters, and the limitation of existing tools, it is
fair to admit that models are useful tools to explore the indirect consequences of disasters but cannot
estimate precisely the total economic cost of a disaster.
15
2.3.3
Rule of thumb
In the framework presented here (see details in Appendix A and B), one dollar of direct loss in
productive capital translates into a decrease in instantaneous (annualized) output that equal to the
average productivity of capital, that is about three times the interest rate (i.e. the marginal productivity
of capital, r), possibly increased by a factor (1+ that represents ripple-effects. Assuming that this
output loss is linearly reduced to zero in N years, total non-discounted output losses are equal to:
(1)
The parameter  represent the “ripple-effects” through the supply chain and infrastructure interruption
effects (see Section 1.2). It represents the lost production from capital that is not affected by the event,
and depends on the ability of the economic system to (1) mobilize existing idle capacity (which depends
on the existence of idle capacity); (2) adjust production network to compensate for damaged production
units (e.g., producers find new suppliers and clients rapidly), (3) channel remaining production toward
its most productive uses (including reconstruction needs), (4) increase imports to compensate for
unavailable supplies. It is likely to be close to zero for relatively limited disasters, and to increase for
large-scale events. It is lower (and possibly negative) if the pre-existing economic situation is depressed,
with under-utilization of production capacity.
In the absence of convincing models or even theory, it can be recommended to take =0, keeping in
mind that we are disregarding some potentially important effects. In very specific cases, different values
could be used, for instance a -20% value if the shock occurs during a recession with large idle resources
(e.g., the landfall of hurricane Andrew in Florida in 1992), or a +20% if the transport sector is heavily
affected, creating large-scale supply-chain issues (or in case of power generation issues such as in Japan
after the 2011 earthquake and tsunami).
The parameter N is the reconstruction period and it can often be estimated by experts based on past
experience. Note that the reconstruction time is not the time when the observed GDP or output returns
to its pre-disaster value, but is much longer. Indeed, the GDP and output is affected by other
mechanisms, including increase in labour productivity, other investments, and possibly the “stimulus
effect” of the disaster. But the stimulus effect should not be accounted in disaster consequences (see
Section 1.2.3), since it corresponds to benefits that could have been captured in the absence of the
disaster, through a classical stimulus policy.
Taking on example of a disaster that make capital losses equal to $500 million, in a country with a 10%
interest rate, with a reconstruction period that is likely to span over 3 years would lead to output losses
equal to $225 million (i.e. 45% of direct capital losses).
In the context of figure 3 (hurricanes on Louisiana), this rule of thumb reproduces model results
perfectly, assuming that losses lower than $50 billion can be repaired in one year, and that losses
amounting to $100, $150, $200, and $250 billion can be repaired in 5, 10, 12, and 15 years, respectively.
16
With discounting at a rate r, the net present value of output losses can be approximated with9:
(2)
And if we approximate this equation further (assuming rN<<1), we get:
(3)
In this framework, macro-economic resilience is described by two parameters, namely 1/ and 1/N. The
former is an instantaneous resilience, i.e. the ability to limit the magnitude of the immediate loss of
income for a given amount of capital losses; the latter is a dynamic resilience, i.e. the ability to
reconstruct and recover quickly.
2.4 From output losses to welfare losses
Losses in economic output do not affect directly people welfare; for them, what matters most is
consumption. It is thus important to investigate how output losses translate into consumption losses.
And since capital and output losses partly interact, it is incorrect to simply sum them to estimate welfare
losses.
Consider first a scenario in which all losses are repaired instantaneously by reducing consumption and
directing all the goods and services that are not consumed toward reconstruction investments (this is a
scenario where reconstruction capacity is infinite). In this theoretical scenario, there is no output loss
since all asset damages are instantaneously repaired, and N=0 in Eq. (1-3). There are however
consumption losses, since consumption has to be reduced to reconstruct, and this reduction is equal to
the reconstruction value (i.e. the replacement cost of damages capital). In that case, consumption losses
are thus simply equal to the value of lost assets (i.e., direct losses).
Consider now another scenario with no reconstruction, in which output losses are permanent (like in
Figure 1) and all losses in output are absorbed by a reduction in consumption (but no share of income is
used for reconstruction). In that case, consumption losses are equal to output losses (with no
reconstruction), and N is equal to infinity in Eq. (2). The loss in consumption is thus equal to 3(1+)K.
Consumption losses and welfare losses are thus larger than the value of lost assets in a noreconstruction scenario.
In the instantaneous reconstruction scenario, consumption losses are equal to the share of consumption
needed to repair and rebuild, i.e. to direct losses. In the no-reconstruction scenario, consumption losses
9
𝑇
It derives from the application of the relationship (in continuous time): ∫0 𝑒
17
𝑇
𝑇 − 𝑡 𝑑𝑡
𝑇
𝑇
2
.
are equal to output losses, i.e. larger than direct losses.10 As a result, consumption (and welfare) losses
are magnified when reconstruction is delayed or slowed down.11 And in all realistic scenarios where
reconstruction take some time (from months for small events to years for large-scale disasters), welfare
losses are larger than direct losses.
The actual welfare losses are thus the sum of the net present value of reconstruction cost (i.e. direct
capital cost) and the net present value of indirect (output) losses. The reconstruction phase, and the
economic recovery pace, will ultimately determine the final welfare cost of the natural disasters. If
reconstruction is done over N years with investments equal to K/N, the present value of consumption
losses is equal to:
[
−
𝑒
−𝑒
]
(4)
Note the two extreme cases:
- As N tends toward zero, C tends to K.
- As N tends toward the infinity (no reconstruction), C tends to 3(1+)K.
And if we approximate this equation further (assuming rN<<1), we get:
[
(
)]
(5)
And if  = 0, then we have:
[
]
(6)
So in that case, the capital losses are simply increased by a factor rN, equal to the product of the interest
rate by the reconstruction duration (this is only valid if the reconstruction is not too long). It is
noteworthy that this expression highlights an interesting component of macroeconomic resilience: the
interest rate. Indeed, the welfare impacts of disasters will be higher in countries where capital is scarce
and the interest rate (and the marginal productivity of capital) is high.
Taking again the example of a disaster that make capital losses equal to $500 million, in a country with a
10% interest rate, with a reconstruction period that is likely to span over 3 years, the discounted loss of
output is $225 million and the discounted loss of consumption due to reconstruction is $425 million, for
a total net present value of consumption loss equal to $650 million.
Box 2 – Indirect flood losses in Mumbai
10
The reality is more complex that what has been described here because not all output losses are translated into
consumption losses. In practice, the loss in output changes the terms of the inter-temporal investmentconsumption trade-off and translates into ambiguous instantaneous changes in consumption and investment. But
the main conclusions of the analysis are not affected by this complexity.
11
The fact that rapid reconstruction is better for welfare than slow reconstruction – or equivalently, that
reconstruction has a return that is much higher than that of “normal investments” and the interest rate – explains
why reconstruction is usually a priority and crowds out consumption and other investments in the affected region.
18
The ARIO model is then used to assess the output impact that would be caused by different floods,
accounting for the characteristics of the Mumbai region economy. In particular, the model accounts for
the financial constraints that can make it very difficult for poor households to finance the
reconstruction, and can thus slow down the recovery significantly. In the model, the 2005 floods – that
caused direct losses amounting to about $2 billion – led to indirect losses amounting to $425 million, i.e.
18% of direct losses (see figure). In the model, reconstruction is carried out over a 12 months period.
Indirect losses (million USD)
4000
3000
July 2005 event
2000
1000
0
0
2000
4000
6000
Direct losses (million USD)
8000
Figure. Relationship between direct losses due to an event and indirect losses (productive sectors plus
housing sector), with the July 2005 event highlighted.
This number evaluated through a significant modelling exercise is broadly consistent with the rule of
thumb proposed below and the fact that most of the reconstruction occurred in one year. With a
marginal productivity of capital of 12.5% in Mumbai, the rule of thumb for the 2005 event would give:
Y = 3/2 r N K =(3/2) (12.5%) (one year) ($2000) = $375 million USD.
Of course, aggregated numbers hide the very large heterogeneity in indirect losses. As already
mentioned, informal dwellers suffered from losses that were on average equal to their total savings,
suggesting that their ability to invest has been strongly affected. Many of them also report forced
reduced consumption, including of food. However, dwellers with stable jobs in the service sector
reported only a few days of lost work.
3 Reducing disaster losses through higher resilience
The previous section demonstrates that welfare losses will depend on the amount of direct losses, but
also on the ability of the economic system to recover and reconstruct (represented by the reconstruction
period length, N). This ability can be referred to as the macroeconomic resilience of the affected
economy. As discussed in Section 1.2, reconstruction investments – and thus the reconstruction speed –
are constrained by a series of constraints. These constraints reduce the economic resilience and increase
welfare costs. Relaxing them can therefore increase resilience and reduce total disaster losses. This
section reviews a few policies to do so.
19
One source of friction is that consumers, firms and public organizations need time to direct high
amounts of money to reconstruction activities. This constraint is crucial in developing economies
(Benson and Clay, 2004). Insurance can of course play an important role in helping finance
reconstruction (see next section), but access to credit is also extremely important. The second source of
friction is linked the capacity of the economic system to produce enough to satisfy reconstruction needs.
Finally, one constraint to reconstruction is linked to the limited ability of the government to finance
reconstruction, directly when public assets are affected and indirectly through the support the
government provides to private actor to help them rebuild.
3.1 Insurance benefits
Insurance is useful for several reasons. First, it spreads the risk among customers, over space, and over
time. By doing so, it replaces rare and large losses by regular and small payments. If economic agents are
risk averse, they prefer smooth and regular losses to an unknown and potentially large loss, and
insurance increases welfare.
Second, most economic agents are not aware of the risk they are facing (World Bank, 2013, Chapter 2). If
insurance premiums are risk-based (which is not the case in most of the world), they provide an
accessible measure of the level of risk. This signalling effect can help households and businesses make
smarter choices (e.g., settling in lower-risk areas), and be an incentive to risk-mitigation actions (e.g., by
reducing insurance premium if homeowners invest in flood-mitigation). The signalling effect of riskbased insurance premium should not be overstated, however. In many places, it would be too small to
trigger significant behavioural changes.
But most importantly, insurance claims support the affected population after a disaster, reducing
immediate welfare losses and consumption reductions, and allowing a faster and more efficient
reconstruction (by relaxing financial constraints, see (Benson and Clay, 2004)). Faster reconstruction
means that overall output losses are reduced.12 Also, reconstruction needs and lost output and income
have a strong impact on households, firm, and public agency budgets, especially in developing countries,
forcing them to reduce consumption (or non-reconstruction-related investments). The reduction in
consumption can then have second-order consequences on other businesses that will see reduced
demand and may respond by cutting on production and employment, magnifying the negative impact of
the shock further (and affecting firms and households that have not been directly affected).
A last benefit of insurance is linked to the predictability of post-disaster support. Government support is
often necessary to some extent (for instance, after Katrina, the U.S. Federal government partly replaced
insurance for households that had no flood insurance). But when it is ad hoc, government support
creates inefficient uncertainty for economic actors (e.g., because, after an event, they cannot known the
exact scope of the government support they will receive) and can lead to moral hazard (e.g. if
12
In Mumbai, our survey shows that loans was almost absent in the 2005 recovery and reconstruction. An
improved access to credit (especially for the underprivileged) could help during the post-disaster period; e.g. the
government guaranteeing reconstruction loans.
20
households know that they will be compensated by government, they will have little incentive to reduce
their own risks or to pay for insurance). International support (grants or goods provided to affected
people) can help reconstruction, but (in addition to the moral hazard issue) is very volatile and
unpredictable.
Box 3 – The current and potential effect of insurance in Mumbai
We can use the ARIO model to investigate the benefits of insurance by exploring the sensitivity of
indirect losses to the insurance penetration rate (γ) assumed in the model. In ARIO, the insurance
penetration affects the response to the shock through two mechanisms: first, if the insurance
penetration of households is low, they have to pay for their reconstruction (either directly, or by getting
into debt and then paying off later), and reduce their consumption to so. Second, if the insurance
penetration of firms is low, firms have to pay for reconstruction, reducing their profits. As a portion of
these profits normally goes to local households, this affects the household budget, also reducing their
consumption.
The box figure illustrates the effect of insurance penetration13 on the household’s budget, for a July
2005 like flood estimated using the ARIO model. Three scenarios are included: (i) γ=0, equivalent to the
absence of insurance system, but with an access to credit; (ii) the current value of flood insurance
penetration estimated by RMS (γ=0.08 for households, γ=0.15 for firms); and (iii) γ=1, representing the
situation where all the reconstruction is paid for by insurance. Where the budget is positive in the figure,
it can be interpreted as the additional savings of households, and where negative it represents debt.
When insurance penetration is large, households make savings after the disaster. This occurs because in
this case, households are rationed and consume less than what they would like to and as a result, they
involuntarily save money. Variations in household budget affect the local demand, as households in debt
reduce their consumption. This decrease in demand affects local production. Total output losses is
reduced from $455 million in the absence of insurance to $425 million with the current insurance
penetration to $265 million if all losses are covered by insurance.
13
It is important to note that our “insurance penetration rate” does not represent the fraction of the total amount
of houses that is insured (as is often done), but the fraction of the total value of goods that is insured (never equal
to one in the real world, because of ceilings or deductibles).
21
Household budget change (USD)
100
0
-100
 = 1 (full insurance)
-200
RMS estimates
 = 0.08 (households)
 = 0.15 (businesses)
-300
 = 0 (no insurance)
-400
0
12
24
36
48
60
72
Time (months)
84
96
108
120
Figure. Household Budget as a function of time, for 3 different penetration rates. Note : γ=0 (no
insurance), current value estimated by RMS (γ=0.08 for households and γ=0.15 for businesses) and γ=1
(reconstruction is entirely paid by the insurance system).
In sum, insurance yields macroeconomic (at the aggregated output level) and microeconomic benefits
(at the household level). Microeconomic benefits include in particular the avoidance of increased
poverty and inequality, the avoidance of the reduction in food intake (observed for about 40% of
households in Mumbai after the 2005 floods), and facilitate and accelerate reconstruction. Even though
they are difficult to estimate in monetary terms, all these benefits should be taken into account, e.g. in
the cost-benefit analysis of the implementation of a micro-insurance scheme.
3.2 Increase in reconstruction capacity
Another source of friction is that the sectors involved in reconstruction activities have skills, equipment,
access to material, and organizational capacities adapted to the normal state of affairs and cannot face
huge increases in demand. After the French storms in 1999 or after the explosion in a chemical plant in
Toulouse in 2001, reconstruction took several years largely because roofers and glaziers were not
numerous enough.
Indeed, over short timescales, local production capacity is highly constrained by existing capacities,
equipment and infrastructure. Only imports from outside the affected region and postponement of
some non-urgent tasks (e.g., maintenance) can create a limited flexibility over the short-term. Over the
longer term and the entire reconstruction period, which can stretch over years for large-scale events, the
flexibility is much higher: relative prices change, incentivizing production in scarce sectors; equipment
and qualified workers move into the affected region, accelerating reconstruction and replacing lost
22
capacities; and different technologies and production strategies can be implemented to cope with longlasting scarcities.
Large economic losses can be avoided with increased flexibility in the construction sector production
capacity. The flexibility depends heavily on the pre-event conditions; for example, if idle capacities are
present (e.g. unused equipment) they can be mobilized to cope with the disaster (Hallegatte and Ghil,
2008; West and Lenze, 1994), whereas if capacities are fully used then no additional capacity can be
mobilized. The flexibility of the construction section could be enhanced through:
 Enabling qualified workers to settle down temporarily in the affected region (e.g. by providing
working permit or helping workers to find accommodation).
 Organizing and sharing reconstruction resources among regions, states or cities and setting
super-national policies to ensure reconstruction capacity is adequate to cope with possible
disasters.
 Empowering governments to mobilizing their workers (e.g., soldiers) and equipment to speed up
reconstruction.
Past disasters illustrate the barriers to efficient reconstruction and suggest good practices. For instance,
in the Katrina aftermath, many qualified workers from the entire U.S. moved to New Orleans to help
reconstruct the city and capture higher construction-sector wages. Most of these workers, however, had
to leave the area rapidly because they could not find proper accommodation or because of insufficient
public services. Providing housing to temporary workers, therefore, seems to be extremely important to
speed up reconstruction. Also, these workers left the region because the reconstruction of many
buildings was delayed by legal problems, either due to delays in insurance claim payments or to the slow
approval of building permits. For reconstruction to be as effective as possible, therefore, it seems that
all administrative and legal issues must be solved rapidly, to benefit for the mobilization of internal and
external resources.
Box 4 – Enhancing reconstruction capacity in Mumbai
The ARIO model also allows us to assess the benefits of sets of policies that aim to accelerate
reconstruction. We find that, for the July 2005 event, the indirect effect of the disaster on the local
economy can vary by a factor of 4 (see box figure), depending on the amplitude and quickness of
response of the construction sector. Of course, the ability to reconstruct impacts mostly the
reconstruction duration and through it the rule of thumb proposed in Section 1.3.3.
23
0.8
0.7
Ratio indirect losses / direct losses
{120% ; 3 months }
No overproduction
{ 120% ; 6 months }
0.6
0.5
0.4
0.3
0.2
0.1
{ 150% ; 6 months }
0
{ 150% ; 3 months }
-0.1
0
2000
4000
6000
Direct losses (million USD)
8000
Figure. Indirect losses to direct losses ratio, as a function of the amount of direct losses, for four sets of
adaptation parameters (the first parameter describes the amount of additional production that is
possible, and the second parameter the time it takes to increase production to this level)
3.3 Increase in public expenditures
Government with fiscal space or insurance or other risk sharing mechanisms will be able to increase
public expenditure to deal with the disaster. The benefits from doing so will depend on how these
resources are spent and used.
The first element is of course the management of the emergency situation. Resources are necessary to
(i) maintain the functioning of the national and local authorities (civil servant salaries need to be paid,
for instance); (ii) provide emergency services to the population, for instance through in-kind support
(food, temporary shelters, drugs and medical services, etc.).
Over the long-term and the whole reconstruction phase, a few elements have to be taken into account.
The reconstruction is a period of supply-side constraints in the sectors involved in reconstruction
(especially the construction sector), so increasing public reconstruction expenditure may crowd out
private reconstruction efforts (by firms and households) and increase prices for all. This may or may not
happen, depending on (1) which reconstruction tasks are concerned (there is less crowding out between
highway and housing reconstruction than between public buildings and housing reconstruction), (2) the
situation of the construction sector and the available worker skills.
Public resources can be used to increase supply-side capacity in the reconstruction sector – for instance
by supporting the migration of workers in the affected areas with temporary housing and the rapid
provision of basic services. It can also focus on restoring transport infrastructure to facilitate the import
24
of reconstruction materials, equipment, and skilled workers, and more generally of rationed goods and
services.
Public resources can also be used to help private actors reconstruct and rebuild, especially where
insurance penetration is low. The goal is to accelerate reconstruction (but using private actors to
determine priorities) and mitigate the needed reduction in consumption (see previous section). More
generally, they can support the population through classical safety nets, in particular to help it cope with
lost income. Moreover, they can help the affected population import (possibly at a higher costs) goods
and services that are not produced locally (e.g., through a reduction in import tariffs).
These different aspects can be combined, for instance using cash-for-work programs in which the
affected population get paid for reconstruction work. This approach combines demand- and supply-side
benefits and is very likely to maximize the welfare benefits.
4 Economic resilience and risk management policies
This analysis shows that the welfare cost of a disaster does not depend only on either the physical
characteristics of the event or its direct impacts. Depending on the ability of the economy to cope,
recover and reconstruct, the reconstruction will be more or less difficult, and its welfare effects smaller
or larger. This ability, which can be referred to as the resilience of the economy to natural disasters, is an
important parameter to estimate the overall vulnerability of a population. Here, resilience is
recomposed into two components: instantaneous resilience, i.e. the ability to limit the magnitude of the
immediate loss of income for a given amount of capital losses; and dynamic resilience, i.e. the ability to
reconstruct and recover quickly.
This paper proposes to assess the total consumption loss of a disaster through the following
relationship:
[
(
)]
Where r is the interest rate (a higher interest rate decreases resilience and increases consumption
losses), N is the reconstruction duration (a longer reconstruction duration increases losses), and  is the
factor that increase or decrease immediate output losses and is negative if enough idle resources are
available and positive if ripple-effects impair production of non-affected capital. After a disaster, it is
easy to estimate N, and r is also known. The parameter is more difficult to estimate, and can be taken
at zero as a first-order estimate, except in very specific situation.
To reduce the negative impact on natural disasters on population welfare, a first approach is to reduce
the direct impacts on the economic systems, using for instance better coastal protections and stricter
building norms. But another approach consist of reducing indirect losses through an increase in the
resilience of the socio-economic system, using for instance insurance scheme or government support to
the affected population.
25
An optimal risk management strategy is very likely to include measures targeting direct impacts (disaster
risk reduction actions) and measures targeting indirect impacts (resilience building actions); see an
example in Figure 4. For instance:



Frequent low-impact events (like the floods that occur several times a year in Mumbai) can be
avoided co-effectively thanks to improved drainage;
For rarer events that cannot be avoided through improved drainage or at an excessive cost (i.e.,
heavy precipitations), population information, zoning and land-use plans could reduce the
exposure (i.e. the population and assets at risk) by preventing inhabitant to settle in flood-prone
zones and favouring safe settlement locations.
For exceptional floods that cannot be avoided with drainage or zoning (because it would
prevent construction is very large areas, at an unacceptable cost in a rapidly growing city), early
warning and evacuation can reduce human consequences, and reduced building vulnerability,
support for reconstruction, and insurance can mitigate economic losses through improved
resilience.
There is no “optimal” risk management policy mix, and different approaches are possible. To minimize
the cost of risk management, and maximize its benefits, the different policies of the mix should be
designed together. With very strong physical protections – like in the Netherlands – there are limited
needs in terms of evacuation system, insurance regulations and building norms. Where financial
constraints make such protection unaffordable – like in Bangladesh – it is all the more critical to
implement an efficient early warning and evacuation scheme (Hallegatte, 2012).
Figure 4: An example of risk-management policy mix, in which: (1) physical protections avoid
frequent events, (2) land-use planning limit losses in these protections are overtopped, (3)
vulnerability-reduction measures such as early warning and building norms minimize the direct
losses from unavoidable disasters; (4) resilience-building measures minimize indirect losses from
unavoidable disasters. Regardless of other measures, crisis management is always potentially
needed to cope with the largest events that exceed the planned capacities.
26
Figure 5. Policies to cope with correlated risks, depending on the spatial correlation
Finally, risk management needs also to be designed as a series of scales (see Figure 5). For idiosyncratic
shocks – car accidents or illness – risk sharing across a small population may be sufficient. But when
correlation increases – say for instance for floods or epidemics – then a large share of the local
population may be affected at the same time, stretching the coping capacity of local systems. This is
when risks need to be transferred to a higher scale, at the regional or national level. And when risks
cannot be shared horizontally – across a larger population in one given year – they need to be shared
across time, through increased savings (with non-correlated returns) and reinsurance schemes.14
In practice, institutional fragmentation and coordination issues makes it difficult to design a risk
management strategy that takes a holistic and integrated view and coordinates across policy options
(figure 4) and across scale (figure 5) (see World Bank, 2013). It makes it even more important to create
the right institutions – able to coordinate across the government, across national and subnational
entities, and between the public and private sectors – and to make risk management enough of a
priority to mobilize the right actors and make the right decisions.
The macro-level resilience that is discussed in this paper is an important factor to determine the welfare
impact of disasters, but it does not include all the factors that have an influence. In particular, this
macro-resilience does not account for micro-resilience, i.e. the ability of households, firms, and
individuals to manage the shock (and its indirect consequences). For instance, the reduction in
consumption due to lost income and reconstruction needs can have direct consequences on individuals
and households, for instance when reduction in food uptake leads to reduced productivity or to children
stunting. These effects are essential to assess the full impact of a disaster on well-being, but are often
invisible in macro-estimates, especially when the poorest are concerned, since their income and assets
are negligible at the aggregate level. The benefits from insurance, for instance, arise probably more from
14
Reinsurance firms receive insurance premiums every year and save (and invest) them to be able to pay claims
when a disaster hits. As such, reinsurance premiums are a form of savings.
27
such micro-benefits than from the macro-effects discussed earlier (even though the latter is not
negligible and should not be overlooked). The macro-level concept of economic resilience presented
here should thus be completed with other aspects regarding the ability of firms to cope (see a
framework to define firm resilience in Rose and Krausmann, 2013) and the ability of household to cope
(see for instance Adger et al., 2002; Morris et al., 2002; Adger, 1999).
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6 Appendix A: The use of classical production function leads to
underestimating output losses
There are several possible biases resulting from disaster modelling using classical production function
(Hallegatte et al., 2007). Production functions are classically used in economics to relate the inputs
and the outputs of a production process. Often, the production function takes as inputs the amount
of labor used in the production process (referred to as L) and the amount of capital (i.e., the value of
all equipment used in the process, referred to as K), and gives the value of the production (expressed
as Y):
Y = f(L,K) .
Disasters mainly destroy the stock of productive capital and a natural modelling option to represent
their consequences is to consider that they reduce instantaneously the total productive capital (K0 →
K0 − ΔK). Figure 6 illustrates several ways of assessing the impact on production. The figure
represents the production Y as a function of capital K. The production function is the blue line linking
the origin of the graph to the point A. It is assumed that the pre-disaster situation is the point A, with
capital K0 and production Y0.
The impact on production can be estimated using the marginal productivity of capital, i.e. the
interest rate at the optimum. This case is shown in Figure 6 as the point B. Point B is estimated using
the orange line, which is the tangent to the production function at point A; its slope is the marginal
productivity of capital (i.e., how much more production do I get if I increase capital by one unit). The
production Y1 is given by the orange line at the X-coordinate K0 – ΔK, and is the estimated residual
production if the output loss is estimated by multiplying the value of the lost capital ΔK by the
interest rate. It is also what is done when the net present value of all output losses is assumed to be
equal to the value of lost assets.
30
The impact on production can also be estimated using the full production function, and decreasing
the amount of capital from K0 to K0 – ΔK. This is what is shown by the point C in the figure. The point
C gives the value of production Y2 given by the production function, i.e. f(L, K0 – ΔK). This option,
however, amounts to assuming that only the least-productive capital has been affected. Obviously,
this is not the case: when a disaster hits, it destroys the capital indiscriminately, not only the least
efficient capital.
Because of decreasing returns in the production function, using a classical production function
amounts to assuming that capital destructions affect only the less efficient capital. In a Cobb–Douglas
setting (Y=ALλKμ), indeed, the after-disaster production would be Y2=ALλ(K0−ΔK)μ. Since μ is classically
estimated around 0.3, an x% loss of equipment would reduce the production by a factor (μ x), i.e.
approximately (0.3 x) % (see figure below).
To account for the fact that disasters affect the capital independently of its productivity, Hallegatte et
al. (2007) propose to modify the Cobb–Douglas production function by introducing a term ξK, which is
the proportion of non-destroyed capital. This new variable ξK is such that the effective capital is
K=ξK·K0, where K0 is the potential productive capital, in absence of disaster. The new production
function is
Y3 = ξK f(L,K0) = A ξK Lλ K0μ
.
With this new production function, an x% destruction of the productive capital reduces production
by x%, and the loss in output is approximately equal to 3.3 times (=1/μ) the loss of asset estimated
using the normal production function. In Figure 6, the new production function is the red line and the
new production Y3 is given by the point C’.
Another bias arises from the aggregation of many different types of capital within only one variable –
capital K – in economic models. If the function f(L,K) is replaced by a function with two types of
capital f(L,K1,K2), the impact of disasters can change dramatically. In particular, because of decreasing
returns in K1 and K2, the impact of a given loss K=K1+K2 depends on how losses are distributed
across the two capitals. The loss in output is larger if all losses affect one type of capital, compared
with a scenario where the two capitals are more homogeneously affected. As a result, disaster loss
estimates can be dependent on the aggregation level of the economic models used to assess them:
the more disaggregated the model is, the more likely it is that one type of capital is heavily affected,
leading to large output losses. The use of production functions may create another problem:
production functions assume that the output of the production process is continuous in K and L. In
reality, there are discontinuities in the production function: the loss of a segment of a road can make
the entire road impracticable and useless; damages to one small piece of equipment in a factory can
make it unable to produce the final product, etc. So a small ΔK can lead to a large loss in output, if
the complementary of different capital items is taken into account. This is especially true when large
network infrastructure is concerned.
31
Production
A
Y0
B
Y1
Y2
Y0
C
Y3
C’
O
K0
K0-K
Capital
Figure 6. Production with respect to productive capital for different modelling assumptions.
One way of investigating these two issues is to assume that there are two categories of capital, K1 and
K2, that are not substitutable (i.e. the production function is a Leontieff function with decreasing
returns):
[
]
(1)
K1 and K2 could be interpreted as two segments of a road, for instance: if one segment is completely
destroyed, the second segment productivity falls to zero, and the total capacity of the road is given by its
segment with the lowest capacity.
Total capital is
. At the optimum, we have
(
2
, and:
)
(2)
(with j equal to 2 when i is equal to 1, and vice-et-versa). Assuming that capital K is distributed optimally
across K1 and K2, the production function becomes:
[
2
2
]
(3)
This production function is equivalent to a classical Cobb-Douglas function (assuming that the labor
input is fixed and included in the parameters). In fact, one assumption in a production function is that
capital can be aggregated into a unique variable K, assuming that capital is then optimally distributed
across categories of capital (i.e. across sectors, technologies, localization, etc.).
The return on capital is equal to the interest rate plus the depreciation rate:
32
(4)
This relationship gives the optimal amount of capital:
̅
Where
[
2
2
(
)
(5)
] .
If only Ki is affected by a disaster, then Ki< Kj, and the production is driven by Ki only and becomes:
[
]
(6)
And the loss in output from a marginal loss of Ki is:
(7)
Replacing Ki using Eqs. (2) and (5), we get:
(
)
This can be generalized for case with N categories of capital into:
(
∑
∑
−
)
If i is really small, the marginal productivity of capital Ki can be extremely high, much higher than the
marginal capital productivity given by Eq. (4). This case is somewhat extreme because the different
categories of capital are assumed non-substitutable, but the qualitative result remain valid with higher
substitutability: considering disaggregated capital categories with imperfect substitutability15, a disaster
would break the assumption that the total amount of capital is optimally distributed across these
categories, increasing the marginal productivity of destroyed capital (and as a result, the marginal
productivity of reconstruction).
7 Appendix B. Quantity and prices in disaster aftermaths
Figure 7 is a classical quantity-price plot, showing the long-term demand and supply curves for a goods
or service aggregated at the macroeconomic level. The green line is the demand curve: it shows how the
quantity demanded by consumers decreases when the price increases. The blue curve is the pre-disaster
(long-term) supply curve: it shows how the quantity produced increases with the price (or, equivalently,
the price asked by producers to produce a given quantity). The point A is the intersection of demand and
15
The only case where this result does not hold is when the production function is in the form: Y=f(K1+K2+…+Kn).
33
supply and shows the price and quantity that clear the market (at that point, supply equals demand).
The economic “surplus” is the area ADE. The consumer surplus is the upper area (AFE) and the producer
surplus the bottom area (AFD).
The red line is the short-term supply curve after the disaster: because of damages, production cannot
exceed 1, and the supply curve becomes vertical at this level (whatever the price consumers are ready
to pay, producers cannot produce more than 1). If the market clears, the new equilibrium is reached at
point B, where the quantity is reduced to 1 and the price increases to p1.
In classical economic reasoning, the move from A to B is reducing the pre-disaster surplus ADE to the
area BCDE. In other terms, the surplus loss is ABC. But this would be correct only if firms were deciding
to reduce production from 0 to 1 and to reduce the expenditure needed to produce Q0. If firms decided
to reduce investment and production capacity from 0 to 1, they would reduce their sales from p0 0 to
p1 1, and reduce their production expenses from the area ODA 0 to the area ODC 1.
Price p
E
Supply curve
(post-disaster)
Supply curve
(pre-disaster)
B
p1
p0
A
F
Demand curve
pC
C
D
O
1
uantity
0
Figure 7. Supply and demand curves in the pre- and post-disaster situations.
When a disaster hits, however, the sales are reduced from p0 0 to p1 1, but the expenses are not
reduced from the area ODA 0 to the area ODC 1. This is because firm expenses have three components:
intermediate consumptions, capital expenses, and labor. The reduction in intermediate consumptions
translates into a loss of output for another firm, so at the macroeconomic level, a reduction in
intermediate is not a gain. Reduction in labor expenditures is also a loss for workers, so it should not be
counted at the macroeconomic level (unless, workers can find instantaneously another job, which is
mostly not the case in disaster contexts). Finally, when a disaster reduces the production capacity from
34
to 1, it does not do so by reducing capital expenses, but by damaging existing capital. If a firm at a
loan to pay for its capital (factory, equipment, etc.), the capital is destroyed but the loan is still there. In
other terms, the capital expenditures are not reduced by the disaster.
0
So to assess the disaster impact on welfare over the short-term, it makes sense to consider the area
0AB 1 (and not the area ABC as in classical long-term welfare analysis). If the price is unchanged, then
the impact can be estimated as p0 (i.e. the loss of output). If the price change is significant, then it is
necessary to take it into account, but it is challenging because the shape of the form 0AB 1 is complex.
A linear assumption would simply be: (p0 + p1)  /2.
35